# 定义BasicBlock
import torch
from torch import nn

# 封装下3x3卷积层（卷积层的bias置为False是因为卷积层后面要加BN层，因此这里的bias不需要）
# Conv2d函数的具体参数说明可参见Pytorch官方手册https://pytorch-cn.readthedocs.io/zh/latest/package_references/torch-nn/#_1
def con3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)

# 封装下1x1卷积层
def con1x1(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kenerl_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width!= 64:
            raise ValueError("BasicBlock only supperts groups=1 and base_width=64")
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supperted in BasicBlock")

        # 下面定义BasicBlock中的各个层
        self.main = nn.Sequential(
            con3x3(inplanes, planes, stride),
            norm_layer(planes),
            nn.ReLU(inplace=True),
            con3x3(planes, planes),
            norm_layer(planes)
        )
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    # 定义前向传播函数将前面定义的各层连接起来
    def forward(self, x):
        identity = x
        out = self.main(x)
        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)
        return out


# 下面定义Bottleneck层
class Bottleneck(nn.Module):
    expansion = 4
    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # 定义Bottleneck中各层
        self.main = nn.Sequential(
            con1x1(inplanes, width),
            norm_layer(width),
            nn.ReLU(inplanes=True),
            con3x3(width, width, stride, groups, dilation),
            norm_layer(width),
            nn.ReLU(inplanes=True),
            con1x1(width, planes * self.expansion),
            norm_layer(planes * self.expansion),
            nn.ReLU(inplanes=True)
        )
        self.downsaple = downsample
        self.stride = stride

    def forward(self, x):
        identity = x
        out = self.main(x)
        if self.downsaple is not None:
            identity = self.downsaple(x)

        out += identity
        out = nn.ReLU(out, inplace=True)
        return out

# 定义ResNet类
class ResNet(nn.Module):
    def __init__(self, block, layer, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None"
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.main = nn.Sequential(
            nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False),
            norm_layer(self.inplanes),
            nn.ReLU(self.inplanes),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            self._make_layer(block, 64, layer[0]),
            self._make_layer(block, 128, layer[1], stride=2,
                             dilate=replace_stride_with_dilation[0]),
            self._make_layer(block, 256, layer[2], stride=2,
                             dilate=replace_stride_with_dilation[1]),
            self._make_layer(block, 512, layer[3], stride=2,
                             dilate=replace_stride_with_dilation[2]),
            nn.AdaptiveAvgPool2d((1, 1))
        )
        self.fc = nn.Linear(512 * block.expanion, num_classes)
        # 定义初始化方式
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_nomal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsaple = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expanion:
            downsaple = nn.Sequential(
                con1x1(self.inplanes, planes * block.expanion, stride),
                norm_layer(planes * block.expanion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsaple, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expanion
        for _ in range(1, block):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilate=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.main(x)
        out = torch.flatten(out, 1)
        out = self.fc(out)
        return out